Master Thesis

P-release kinetic as a predictor for P-availability in the STYCS Trials

Marc Jerónimo Pérez y Ropero

Introduction

  • In my Internship I studied the current GRUD, particularly Mg, P and K

  • Fertilizer requirement models imply \(Y\sim STP + Clay\) & \(P-\text{Export}\sim STP + Clay\)

  • Currently only stationery measurement of STP are considered

  • Could a kinetic desorption-model better explain the soil status and yield data?

    GRUD 2017

Experimental Setup

  • LTE STYCS, all treatment conditions equal except P-fertilization, which is in 6 Levels, 3 were considered(\(P0\),\(P100\),\(P166\))
  • 5 Sites regarded; Cadenazzo, Ellighausen, Rümlang-Altwi, Oensingen, Zürich-Reckenholz
  • 5 Sites, 4 blocks per site, 6 Treatment-Levels, 4 Repetitions
  • Years 2017-2022 were observed, kinetic data was collected for year 2022 and used to predict 2017-2022

Kinetic Model

Flossmann & Richter conducted in 1982 experiments that should: - improve the classification of P-supply in soils - work in tandem with a current STP-method e.g. CAL or Olsen

The net-desorption was observed, a kinetic of first order was assumed: \[\frac{dP}{dt}=k\times(P^S-P)\] When solved with \(P(0)=0\), the following equation was obtained: \[P(t)=P^S\times(1-e^{-kt})\] The researchers estimated \(P^S=P_\text{CAL/Olsen}-P_{H_2O}\) and linearized as follows: \[log(1-\frac{P(t)}{P^S})=-kt\] \(PS\), \(k\) and \(k*PS\) were extracted, \(k*PS\) being the average net-release speed.

Kinetic-Experiment Setup

Could a kinetic desorption-model better explain the soil status and yield data?

Relevant Variables

Soil Variables:

  • \(P-CO_2\) & \(P-AAE10\) stand for the GRUD STP-measurements in [\(g~P/kg ~ Soil\)]
  • \(k\)(\(s^{-1}\)) can be interpreted as the relative speed of net-desorption of orthophosphate
  • \(k*PS\)(\(g~Ps^{-1}\)) can be interpreted as the average net-release speed
  • \(PS\)(\(mg~P/L~H_2O\)) is the equilibrium concentration of \(PO_4^{3-}\) of the net-desorption experiment
  • From the 0-20cm Horizon: Clay-, Silt-,\(C_{org}\)-content and pH

Yield Variables:

  • For a year \(X\) and crop \(C\) \(Y_{main-rel}\) stands for \(Y_{main-rel}:=Y_C^{X}/mean(Y_C~\text{in year}~X~\text{in CH})\)
  • For every year:site:crop combination the yield was normalised using: \(Y_\text{norm}:=Y/median(Y_{P166})\)
  • The P-Export was calculated as the P-Uptake of the main product
  • The P-Balance was calculated as the difference \(P_{Fertilized}-\text{P-Export}\)

Research Questions

  • I: Is the method presented by Flossmann and Richter (1982) with the double extraction replicable with the soils from the STYCS-trial?
  • II: How do GRUD-measurements of STP correlate to the soil properties \(C_\text{org}\)-content, clay-content, silt-content and pH?
  • III: Are the kinetic coefficients \(k\) and \(PS\) correlated to soil properties?
  • IV: How well can current GRUD methods of STP (\(P-CO_2\) & \(P-AAE10\)) predict the Yield-parameters, P-Export and P-Balance?
  • V: How well can the kinetic parameters \(k\) & \(PS\) predict Yield-parameters, P-Export and P-Balance?

QI: Replicability of kinetic model in STYCS

QII & III: STP, k & PS correlate to soil properties?

The following random structure was chosen:

(1|year) + (1|Site) + (1|Site:block) + (Treatment|Site)

Do P-CO2, P_AAE10, k and PS correlate with soil characteristics?

Coefficient Table for Soil Covariates. Significant codes: 0 '***' 0.001 '**' 0.01 '*' 0.05
Covariate PS k log.k.PS. k.log.PS. CO2 AAE10 fail
(Intercept) ***-4.271 -0.159 ***-6.881 0.049 -1.092 -0.043 ***124.095
k 72.766
k:log(PS) 36.002
log(PS) 1.355
soil_0_20_clay 0.012 -0.004 -0.007 * 0.012 -0.016 0.010
soil_0_20_Corg ***0.567 -0.003 0.216 0.062 * 0.490 * 0.379
soil_0_20_pH_H2O -0.003 ** 0.035 0.174 ** -0.086 0.067 * 0.148
soil_0_20_silt -0.021 ** 0.007 0.013 * -0.019 -0.036 0.015
TreatmentP100 ***1.063 0.002 ** 1.056 ***0.195 ** 0.703 ***0.744
TreatmentP166 ***1.841 -0.031 ***1.633 ***0.412 ***1.271 ***1.157
R2m 0.858 0.399 0.666 0.678 0.661 0.519 0.029
R2c 0.950 0.697 0.912 0.833 0.782 0.905 0.800

QIV & V: Correlation k, PS & STP to Yield and P-metrics

Yield model summary:

Coefficient Table for Yield Variables. Significant codes: 0 '***' 0.001 '**' 0.01 '*' 0.05
Covariate CO2_Ynorm AAE10_Ynorm Grud_Ynorm Kin_Ynorm CO2_Yrel AAE10_Yrel Grud_Yrel Kin_Yrel
(Intercept) ***1.038 ***0.463 ***0.970 ***0.881 ***98.786 ***67.321 ***70.659 ***124.095
k * 1.558 72.766
k:log(PS) ** 0.680 36.002
log(PS) -0.024 1.355
log(soil_0_20_P_AAE10) ***0.140 0.020 ** 8.788 8.099
log(soil_0_20_P_CO2) ***0.163 0.095 6.190 1.155
log(soil_0_20_P_CO2):log(soil_0_20_P_AAE10) 0.021
TreatmentP100 6.546 4.352 4.062
TreatmentP166 4.838 2.232 1.599
R2m 0.226 0.203 0.224 0.194 0.080 0.098 0.098 0.029
R2c 0.432 0.445 0.439 0.409 0.576 0.582 0.582 0.800

P-Export model summary:

Coefficient Table for P-export. Significant codes: 0 '***' 0.001 '**' 0.01 '*' 0.05
Covariate CO2_Pexport AAE10_Pexport Grud_Pexport Kin_Pexport
(Intercept) ***24.761 9.594 ** 24.908 ***36.785
k 23.494
k:log(PS) 10.824
log(PS) 2.380
log(soil_0_20_P_AAE10) 3.385 -0.043
log(soil_0_20_P_CO2) ** 4.757 * 4.783
TreatmentP100 2.085 3.022 2.097
TreatmentP166 1.150 3.338 1.166
R2m 0.076 0.074 0.076 0.039
R2c 0.577 0.542 0.577 0.751

P-balance model summary:

Coefficient Table for P-balance. Significant codes: 0 '***' 0.001 '**' 0.01 '*' 0.05
Covariate CO2_Pbalance AAE10_Pbalance Grud_Pbalance Kin_Pbalance
(Intercept) ***-24.532 -8.080 * -24.460 ***45.816
k 81.169
k:log(PS) 31.798
log(PS) ***19.365
log(soil_0_20_P_AAE10) -3.680 -0.022
log(soil_0_20_P_CO2) * -5.148 -5.138
TreatmentP100 ***30.965 ***29.964 ***30.974
TreatmentP166 ***54.052 ***51.704 ***54.066
R2m 0.628 0.641 0.627 0.521
R2c 0.808 0.798 0.807 0.784

Key Findings

  • The estimation \(PS=P_{Olsen}-P_{H_2O}\) did not deliver reasonable and significant models
  • P-CO2 did not correlate with clay-content
  • k does not correlate with Treatment but with pH and silt-content
  • \(k*log(PS)\) had significant effects for clay- and silt-content as well as pH
  • PS was the covariate best predicted by soil properties: \(R^2_m=0.858\)
  • \(k*log(PS)\) and \(k\) showed the strongest effects in the prediction of Ynorm and Yrel
  • P-AAE10 did show a significant effect in prediction of Yrel
  • P-CO2 did show strong effects in both predicting Pexport and Pbalance (however negative in Pbalance)
  • \(PS\) showed the strongest effect in predicting P_balance